# scipy.interpolate.InterpolatedUnivariateSpline¶

class scipy.interpolate.InterpolatedUnivariateSpline(x, y, w=None, bbox=[None, None], k=3)

One-dimensional interpolating spline for a given set of data points.

Fits a spline y=s(x) of degree k to the provided x, y data. Spline function passes through all provided points. Equivalent to UnivariateSpline with s=0.

Parameters : x : array_like input dimension of data points – must be increasing y : array_like input dimension of data points w : array_like, optional Weights for spline fitting. Must be positive. If None (default), weights are all equal. bbox : array_like, optional 2-sequence specifying the boundary of the approximation interval. If None (default), bbox=[x[0],x[-1]]. k : int, optional Degree of the smoothing spline. Must be <= 5.

UnivariateSpline
Superclass – allows knots to be selected by a smoothing condition
LSQUnivariateSpline
spline for which knots are user-selected
splrep
An older, non object-oriented wrapping of FITPACK
BivariateSpline
A similar class for two-dimensional spline interpolation

Notes

The number of data points must be larger than the spline degree k.

Examples

```>>> from numpy import linspace,exp
>>> from numpy.random import randn
>>> from scipy.interpolate import UnivariateSpline
>>> x = linspace(-3, 3, 100)
>>> y = exp(-x**2) + randn(100)/10
>>> s = UnivariateSpline(x, y, s=1)
>>> xs = linspace(-3, 3, 1000)
>>> ys = s(xs)
```

xs,ys is now a smoothed, super-sampled version of the noisy gaussian x,y

Methods

 __call__(x[, nu]) Evaluate spline (or its nu-th derivative) at positions x. derivatives(x) Return all derivatives of the spline at the point x. get_coeffs() Return spline coefficients. get_knots() Return the positions of (boundary and interior) get_residual() Return weighted sum of squared residuals of the spline integral(a, b) Return definite integral of the spline between two roots() Return the zeros of the spline. set_smoothing_factor(s) Continue spline computation with the given smoothing

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